Second European School on Bioinformatics, CMBI, Nijmegen, Netherlands, January 22-25, 2005

1. Proteomics Analysis and integration of large-scale data sets Lars Juhl Jensen EMBL Heidelberg

3. Part 1 Methods for predicting protein-protein interactions Lars Juhl Jensen EMBL Heidelberg

6. What is STRING? Genomic neighborhood Species co-occurrence Gene fusions Database imports Exp. interaction data Microarray expression data Literature co-mentioning

7. Genomic context methods © Nature Biotechnology, 2004

8. Inferring functional modules from gene presence/absence patterns T rends in Microbiology

9. Inferring functional modules from gene presence/absence patterns T rends in Microbiology

10. Inferring functional modules from gene presence/absence patterns T rends in Microbiology

11. Inferring functional modules from gene presence/absence patterns T rends in Microbiology Resting protuberances Protracted protuberance Cellulose © Trends Microbiol, 1999 Cell Cell wall Anchoring proteins Cellulosomes Cellulose The “Cellulosome”

12. Formalizing the phylogenetic profile method Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps

13. Inferring functional associations from evolutionarily conserved operons Identify runs of adjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in other species

14. Predicting functional and physical interactions from gene fusion/fission events Find in A genes that match a the same gene in B Exclude overlapping alignments Calibrate against KEGG maps Calculate all-against-all pairwise alignments

15. Integrating physical interaction screens Make binary representation of complexes Yeast two-hybrid data sets are inherently binary Calculate score from number of (co-)occurrences Calculate score from non-shared partners Calibrate against KEGG maps Infer associations in other species Combine evidence from experiments

16. Mining microarray expression databases Re-normalize arrays by modern method to remove biases Build expression matrix Combine similar arrays by PCA Construct predictor by Gaussian kernel density estimation Calibrate against KEGG maps Infer associations in other species

18. Non-linear normalization of intensities and correction for spatial effects Downloaded SMD data After intensity normalization Spatial bias estimate After spatial normalization

19. Co-mentioning in the scientific literature Associate abstracts with species Identify gene names in title/abstract Count (co-)occurrences of genes Test significance of associations Calibrate against KEGG maps Infer associations in other species

21. The power of cross-species transfer and evidence integration

22. The power of cross-species transfer and evidence integration

23. The power of cross-species transfer and evidence integration

24. The power of cross-species transfer and evidence integration

25. The power of cross-species transfer and evidence integration

26. The power of cross-species transfer and evidence integration

28. Questions?

29. Part 2 Quality control of high-throughput interaction data Lars Juhl Jensen EMBL Heidelberg

34. Binary representations of purification data © Drug Discovery Today: TARGETS, 2004

43. Questions?

44. Part 3 Prediction protein features and function Lars Juhl Jensen EMBL Heidelberg

53. Prediction performance on cellular role categories © Journal of Molecular Biology, 2002

54. © Journal of Molecular Biology, 2002

55. An example – 1AOZ vs. 1PLC scoring matrix: BLOSUM50, gap penalties: -12/-2 15.5% identity; Global alignment score: -23 10 20 30 40 50 60 1AOZ SQIRHYKWEVEYMFWAPNCNENIVMGINGQFPGPTIRANAGDSVVVELTNKLHTEGVVIH .. .. : ... . . ..: . :...: . .: ...:. 1PLC ---------IDVLLGA---DDGSLAFVPSEFS-----ISPGEKIVFK-NNAGFPHNIVFD 10 20 30 40 70 80 90 100 110 120 1AOZ WHGILQRGTPWADGTASISQCAINPGETFFYNFTVDNPGTFFYHGHLGMQRSAGLYGSLI .: :. . . : . :::: .. . .:. : : ::. :.. 1 PLC EDSI-PSGVDASKISMSEEDLLNAKGETFEVALSNKGEYSFYCSPHQG----AGMVGKVT 50 60 70 80 90 1AOZ VDPPQGKKE :. 1PLC VN-------

56. An enzyme and a non-enzyme from the Cupredoxin superfamily

59. Questions?

60. Part 4 Qualitative modeling of the of the yeast cell cycle Lars Juhl Jensen EMBL Heidelberg

64. Getting the parts list yeast culture Microarrays Gene expression Expression profile Ulrik de Lichtenberg, CBS, DTU Lyngby Cho et al. & Spellman et al. 600 periodically expressed genes (with associated peak times) that encode “dynamic proteins” The Parts list New Analysis

66. Interactions are close in time Observation: Interacting dynamic proteins typically expressed close in time Ulrik de Lichtenberg, CBS, DTU Lyngby © Science, 2005

67. Static proteins play a major role Observation: Static ( scaffold ) proteins comprise about a third of the network and participate in interactions throughout the entire cycle Ulrik de Lichtenberg, CBS, DTU Lyngby © Science, 2005

68. Just-in-time synthesis? yes and no! Observation: The dynamic proteins are generally expressed just before they are needed to carry out their function, generally referred to as just-in-time synthesis But, the general design principle seems to be that only some key components of each module/complex are dynamic This suggests a mechanism of just-in-time assembly or partial just-in-time synthesis Ulrik de Lichtenberg, CBS, DTU Lyngby © Science, 2005

69. Network as a discovery tools Observation: The network places 30+ uncharacterized proteins in a temporal interaction context. The network thus generates detailed hypothesis about their function. Observation: The network contains entire novel modules and complexes. Ulrik de Lichtenberg, CBS, DTU Lyngby © Science, 2005

70. Network Hubs: “Party” versus “Date” “ Date” Hub: the hub protein interacts with different proteins at different times. “ Party” Hub: the hub protein and its interactors are expressed close in time. Ulrik de Lichtenberg, CBS, DTU Lyngby © Science, 2005

72. A neural network strategy for prediction of cell cycle related proteins Ulrik de Lichtenberg, CBS, DTU Lyngby

73. Prediction of cell cycle related proteins from sequence derived features Ulrik de Lichtenberg, CBS, DTU Lyngby © Journal of Molecular Biology, 2003

74. Evaluating the performance Ulrik de Lichtenberg, CBS, DTU Lyngby

75. Ulrik de Lichtenberg, CBS, DTU Lyngby

76. The yeast cell cycle in feature space © Journal of Molecular Biology, 2003 Ulrik de Lichtenberg, CBS, DTU Lyngby

80. Questions?

83. Thank you!